Intelligent logging

ABSTRACT

An on-board computing system for determining an opportune time to log data into a first memory. A sensor system collects data of a vehicle&#39;s environment. A controller of the on-board computing system logs the data to a first memory when it determines an opportune time to log data to the first memory. The controller holds data in a second memory if it determines it is not an opportune time to log data into the first memory. The controller resumes logging data to the first memory when an opportune time presents itself.

BACKGROUND

The present disclosure relates to systems, components, and methodologiesfor logging data through sensors in a vehicle. More particularly, thepresent disclosure relates to systems, components, and methodologiesthat improve logging data through sensors in a vehicle in situationssuch as a high traffic density environment.

SUMMARY

According to the present disclosure, systems, components, andmethodologies are provided for logging data in an intelligent manner toreduce the computational load on an on-board computing system.

In illustrative embodiments, an on-board computing system of anautonomous vehicle assesses the criticality of a situation beforedetermining whether or not to log data to a first memory or hold data ina second memory until a more opportune time presents itself to log datato a first memory. This is because logging data to a first memory thatmay be embodied as a hard drive is orders of magnitude slower thanholding data in a second memory that may be embodied as RAM. The secondmemory in this case can be accessed hundreds of times faster than thefirst memory. Therefore, in potential critical situations when a fastercycle of processing is needed, the second memory holds the data in orderto speed up the process of data logging during that duration in order toaddress the potential critical situation.

The assessment of criticality of the situation may be based upon adetermination if a number of objects in the near vicinity of theautonomous vehicle exceed a predetermined threshold value. Theassessment of criticality of the situation may also be based upon adetermination if a predicted time to collision of the autonomous vehiclewith another object falls below a predetermined threshold value. Ifthere is a determined potential critical situation, the on-boardcomputing system holds data in the second memory to reduce thecomputational load and allow the on-board computing system to reactfaster to a potential critical situation.

Additional features of the present disclosure will become apparent tothose skilled in the art upon consideration of illustrative embodimentsexemplifying the best mode of carrying out the disclosure as presentlyperceived.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The detailed description particularly refers to the accompanying figuresin which:

FIG. 1 depicts an autonomous vehicle having an autonomous driving systemin accordance with the present disclosure, and shows that the autonomousdriving system includes cameras that collect image data that can be usedto assess the criticality of the situation;

FIG. 2 is a diagrammatic view of an on-board computer system inaccordance with the present disclosure that includes one or more datacollectors configured to collect data related to vehicle environment,one or more modules representing the various systems of the vehicle, acontroller to process the data collected and log the data into firstmemory after an assessment of the criticality of the situation;

FIG. 3 depicts a stretch of highway with a group of secondary vehiclesin a traffic jam wherein the autonomous vehicle is entering the highwayand analyzing the secondary vehicles to assess the criticality of thesituation in order to determine whether or not to log data of thevehicle environment in accordance with the present disclosure;

FIG. 4 is a detail view of FIG. 3 taken from the perspective of theautonomous vehicle which shows one of the secondary vehicles from thegroup of secondary vehicles, wherein identifying features includinglight emissions, thermal emissions, audio emissions, radio emissions,and other identifiers are collected by the primary vehicle to log dataof the vehicle environment and analyzed to determine the distance,relative velocity, relative acceleration, and predicted time tocollision with the secondary vehicle in order to assess the criticalityof the situation in accordance with the present disclosure;

FIG. 5 is a partial perspective view of an interior of the autonomousvehicle which depicts a navigation screen displaying the presentlocation of the autonomous vehicle, wherein the navigation screen isdisplaying messages to indicate to a user of the autonomous vehicle thata traffic jam is ahead and as a result may halt logging of data tomemory until an assessment of the criticality of the situation isresolved in accordance with the present disclosure;

FIG. 6 depicts a stretch of residential roadway with a group ofsecondary vehicles parked along a side of the roadway, wherein theautonomous vehicle is driving along the roadway to park along the sideof the roadway and analyzing the secondary vehicles to assess thecriticality of the situation in order to determine whether or not to logdata of the vehicle environment in accordance with the presentdisclosure;

FIG. 7 is a detail view of FIG. 6 taken from the perspective of theautonomous vehicle which shows one of the secondary vehicles from thegroup of secondary vehicles, wherein identifying features regarding thesecondary vehicle's status including light emissions, thermal emissions,audio emissions, radio emissions, and other identifiers are collected bythe autonomous vehicle to determine a status of the secondary vehicle tolog data of the vehicle environment and analyzed to determine thedistance, relative velocity, relative acceleration, and predicted timeto collision with the secondary vehicle in order to assess thecriticality of the situation in accordance with the present disclosure;

FIG. 8 is a partial perspective view of the interior of the autonomousvehicle which depict the navigation screen displaying the presentlocation of the autonomous vehicle, wherein the navigation screen isdisplaying messages to indicate to a user of the autonomous vehicle thatpedestrians may be present in the area and as a result may halt loggingof data to memory until an assessment of the criticality of thesituation is resolved in accordance with the present disclosure;

FIG. 9A-9C depicts different scenarios where the assessment of thecriticality of the situation may affect the logging of data of thevehicle environment;

FIG. 9A depicts the autonomous vehicle assessing the criticality of thesituation and determining whether or not to log data of the vehicleenvironment based upon an assessment of the time to collision to aneighboring vehicle;

FIG. 9B depicts the autonomous vehicle assessing the criticality of thesituation and determining whether or not to log data of the vehicleenvironment based upon an assessment of the potential time to collisionto a neighboring vehicle;

FIG. 9C depicts the autonomous vehicle assessing the criticality of thesituation and determining whether or not to log data of the vehicleenvironment based upon an assessment of the number of objects in thenear vicinity; and

FIG. 10 is a flow diagram illustrating a methodology for assessingcriticality of the situation before logging data into memory.

DETAILED DESCRIPTION

The figures and descriptions provided herein may have been simplified toillustrate aspects that are relevant for a clear understanding of theherein described devices, systems, and methods, while eliminating, forthe purpose of clarity, other aspects that may be found in typicaldevices, systems, and methods. Those of ordinary skill may recognizethat other elements and/or operations may be desirable and/or necessaryto implement the devices, systems, and methods described herein. Becausesuch elements and operations are well known in the art, and because theydo not facilitate a better understanding of the present disclosure, adiscussion of such elements and operations may not be provided herein.However, the present disclosure is deemed to inherently include all suchelements, variations, and modifications to the described aspects thatwould be known to those of ordinary skill in the art.

FIG. 1 depicts an autonomous vehicle 10 driving on a roadway 104 havingfour lanes 104 a, 104 b, 104 c, and 104 d. Several neighboring vehicles106, 108, 110, and 112 are driving in proximity of the autonomousvehicle 10, wherein neighboring vehicle 112 carries an exposed cargo114.

The autonomous vehicle 10 may include an on-board computing system 200(depicted in FIG. 2 and to be described in more detail below). Theon-board computing system 200 may include a front camera 212 and a rearcamera 214 that may capture image data 12 of the proximity of theautonomous vehicle 10. Thus, for example, the front camera 212 maycapture image data 12 of the neighboring vehicles 110 and 112, both ofwhich may be located generally forward of the vehicle 10. Similarly, therear camera 214 may capture image data 12 of the neighboring vehicles106 and 108, both of which may be located generally rearward of thevehicle 10.

The on-board computing system 200 may use image data 12 of theneighboring vehicles 106, 108, 110, and 112 to develop an assessment ofcriticality of a given situation to determine if is an opportune time tolog data 12 into a first memory 208 or hold data 12 in a second memory206. The assessment of criticality of the situation may includeidentifying objects in the near vicinity of the autonomous vehicle 10 tosee if the number of objects exceeds a predetermined threshold value.The assessment of criticality of the situation may also includeevaluating a time to collision with a neighboring vehicle 106, 108, 110,or 112 to determine if the time to collision falls below a predeterminedthreshold value. In the illustrative embodiment, if it is deemed not tobe an opportune time to log data 12 into a first memory 208 based on theassessment of criticality because the predetermined threshold value wasexceeded, then second memory 206 may hold data 12 until an opportunetime presents itself, i.e., the threshold is not exceeded. The on-boardcomputing system 200 may also compare the amount of space on secondmemory 206 and the assessment of criticality of the situation todetermine if it is necessary to log data 12 into first memory 208.

As a result, the on-board computing system 200 may provide improvedsafety and efficiency. With respect to safety, the on-board computingsystem 200 enables the autonomous vehicle 10 to log data 12 in asituation where it is less likely to collide with a neighboring vehicle106, 108, 110, or 112. With respect to efficiency, the on-board computersystem 200 logs data 12 in situations that are less computationallyintensive on the processor 204 to process the data 12 and store the data12 into first memory 208.

FIG. 2 is a diagrammatical view of the illustratively embodied on-boardcomputing system 200 in accordance with the present disclosure. Theon-board computing system 200 may include the controller 202 having aprocessor 204, first memory 208, and a second memory 206. In accordancewith a main embodiment, the on-board computing system 200 may alsoinclude a sensor system 210 that includes the previously describedcameras 212, 214, Lidar 216, radar 218, and other sensors 220. Theillustratively embodied on-board computing system 200 may also include asteering and acceleration/braking system 222 and a human machineinterface 224, side mirror adjustment system with proximity detectors, aheadlight control system with proximity detectors, a window controlsystem with proximity detectors, an information and entertainment systemwith proximity detectors, a climate control system with proximitydetectors, a gear and power train adjustment system with proximitydetectors, an audio control system, and a multifunction display controlsystem. Lidar technology collects data using remote sensing technologyto measure distance by illuminating a target with a laser and analyzingthe reflected light.

In other, additional and/or optional embodiments, the other sensors mayinclude, for example, microphones, air and particulate detector, etc.

In accordance with disclosed embodiments, controller 202 may beelectrically coupled to the sensor system 210 and the electrical systems220, 222, 224, 226, 228, 230, 232, and 234. The electrical connectionscan be made using any mechanism known in the art, such as acommunication bus.

The illustratively embodied on-board computing system 200 may use thecontroller 202 to process the electrical systems 222 and 224 and thesensor system 210 to send data 12 to the controller 202 to log intofirst memory 208 or hold data 12 in second memory 206 until theprocessor 204 can log data 12 in first memory 208.

FIG. 3 depicts a highway 300 having an on-ramp 302 connecting with lanes306 of highway 300. An autonomous vehicle 10 entering highway 300 froman on-ramp 302 may capture data 12 regarding secondary vehicles 304 onhighway 300. The data 12 may be used to assess the criticality of thesituation to determine if it is an opportune time to log the data 12into first memory 208 or hold the data 12 in second memory 206 until anopportune time presents itself. For example, if the amount of secondaryvehicles 304 exceeds a predetermined threshold value, then theautonomous vehicle 10 may stop logging data 12 regarding itsenvironment.

In the illustrative embodiment of FIG. 3, data 12 may includeinformation regarding thermal emissions 311, 312, light emissions 313,314, audio emissions 315, and radio emissions 316, 317 from secondaryvehicle 304, as illustrated in FIG. 4. These emissions 311-317 may belogged by the autonomous vehicle 10 to analyze parameters indicative toactivity in a vehicle environment (e.g. the surrounding thermal data,the surrounding light data, the surrounding audio data, etc.). In someembodiments, data 12 regarding multiple secondary vehicles 304 in agroup of adjacent secondary vehicles 304 may be captured to analyze theparameters indicative of the activity in the vehicle environment (e.g.the surrounding thermal data, the surrounding light data, thesurrounding audio data, etc.). The data 12 may be used to determine thedistance, relative velocity, relative acceleration, and predicted timeto collision with the secondary vehicles 304 in order to assess thecriticality of the situation to determine if it is an opportune time tolog data 12 into the first memory 208 or hold data 12 in the secondmemory 206 until an opportune time presents itself as described in thedisclosure.

In the illustrative embodiment, a notification 16 of traffic ahead maybe displayed to a user of the autonomous vehicle 10 if the probabilitythat adjacent secondary vehicles 304 are in a traffic jam, as determinedby the on-board computing system 200, reaches or exceeds a predeterminedthreshold limit as shown in FIG. 5. In some embodiments, a prompt 18 maybe displayed to the user to activate an autonomous driving function ofvehicle 10 which may operate when vehicles 10, 304 are in a traffic jam.In some embodiments, identification of a traffic jam may promptautonomous vehicle 10 to send location data 12 to a server for mappingtraffic patterns. Other uses for identification of traffic jams are alsocontemplated.

FIG. 6 depicts a residential roadway 400 having lanes 406. Autonomousvehicle 10 driving on roadway 400 may capture data 12 regardingsecondary vehicles 404 positioned alongside a curb 402 of right-sidelane 406. An opening 408 for autonomous vehicle 10 to park in may alsobe identified as part of an auto-park function of autonomous vehicle 10if it is determined that secondary vehicles 404 are also parked. Thedata 12 may be used to determine the distance, relative velocity,relative acceleration, and predicted time to collision with thesecondary vehicles 404 in order to assess the criticality of thesituation to determine if it is an opportune time to log the data 12into first memory 208 or hold the data 12 in second memory 206 until anopportune time presents itself. For example, if the amount of secondaryvehicles 404 exceeds a predetermined threshold value then the autonomousvehicle 10 may stop logging data 12 of its environment. However, in anillustrative embodiment, the on-board computing system 200 may determinethat it is safe to log data 12 in first memory 208 if there is a lowpotential for a collision as a result of the secondary vehicle 404.

As shown in FIG. 7, data 12 captured by autonomous vehicle 10 mayindicate that secondary vehicle 404 lacks any active-status indicators,making the probability unlikely that secondary vehicles 404 are apossible object to collide with. As a result, the on-board computingsystem 200 may log data 12 to first memory 208 after assessing thecriticality of the situation. Location data 12 of autonomous vehicle 10may indicate that the autonomous vehicle 10 is travelling on residentialroadway 400, as depicted in FIG. 8, which may confirm that theprobability of secondary vehicles 404 being part of a traffic jam is lowand more likely that the secondary vehicles 404 are actually parked.

In the illustrative embodiment, a notification 17 that pedestrians maybe present may be displayed to a user of autonomous vehicle 10 if it isdetermined by the on-board computing system 200 that secondary vehicles404 are parked on a residential or other non-controlled access roadwayas shown in FIG. 8. A detection that a number of pedestrians may bepresent may cause the on-board computing system 200 to stop logging data12 in first memory 208 and hold data in second memory 206 as a result ofthe number of objects in the vicinity of the autonomous vehicleexceeding a predetermined threshold value. In some embodiments, a prompt19 may be displayed to the user to activate the auto-park function ofvehicle 10 to guide autonomous vehicle 10 into opening 408. In someembodiments, access to the autonomous driving function of autonomousvehicle 10 may be blocked or prohibited if it is determined thatautonomous vehicle 10 is on a residential or other non-controlled accessroadway.

In an illustrative embodiment, the autonomous driving function of aprimary vehicle may use Lidar or radar based cruise control to maintainspacing from other secondary vehicles on the roadway. However, such aLidar or radar based system may not be able to distinguish objectssmaller than a vehicle, such as a pedestrian or bicycle user. In such anembodiment, access to the autonomous driving function may be blocked, orprohibited, if the primary vehicle is on a non-controlled access roadwaywhere pedestrians are likely to be present.

The on-board computing system 200 may include certain components fordetecting and analyzing characteristics of secondary vehicles 304, 404.A sensor system 210 may be provided on autonomous vehicle 10 andconfigured to capture data 12 including emissions 311-317 of secondaryvehicles 304, 404. In an illustrative embodiment, sensor system 210 mayinclude the cameras 212, 214 for obtaining image data 12 regarding lightemissions 313, 314 and image data 12 regarding thermal emissions 311,312, such as through infrared signals for example, and a radio receiverfor obtaining signal data 12 regarding radio emissions 316, 317 comingfrom secondary vehicles 304, 404. Sensor system 210 may be coupled toautonomous vehicle 10 in an area where a wide range of views arevisible, such as, for example, a bumper, hood, roof, side mirror,rear-view mirror, front fascia, or dashboard 13 of autonomous vehicle10, etc.

In some embodiments that include optional sensors, audio emissions 315include passenger voices, such as indicated at 315 in FIG. 4, soundsfrom an entertainment system, engine noise, and braking noise, to name afew. In some embodiments, radio emissions 316, 317 include distancesensor signals, ultrasonic parking signals, blind spot radar, andadaptive cruise control radar such as indicated at 317, wi-fi signals,BLUETOOTH™ signals, cellphone signals, and entertainment system signals,such as indicated at 316, to name a few. In some embodiments, lightemissions 313, 314 may include tail or brake light emissions, such asindicated at 313, headlamp or turn signal emissions, such as indicatedat 314, and internal cabin light emissions, etc. In some embodiments,thermal emissions 311, 312 may include brake heat emissions, such asindicated at 311, engine heat emissions, such as indicated at 312, cabinheat emissions, and exhaust heat emissions, etc.

Moreover, in accordance with such embodiments, a microphone may be usedto detect audio emissions 315 from particular neighboring vehicles.Typical driving patterns of secondary vehicles 304 or 404 may beinformed by audio emissions 315. For example, if a neighboring vehicle304 or 404 may be emitting sounds suggesting engine trouble, theon-board computing system 200 may determine that the neighboring vehicle304 or 404 could suddenly decelerate or pull over. If a neighboringvehicle may be emitting sounds suggesting loud music, the on-boardcomputing system 200 may determine that a driver of the secondaryvehicle 304 or 404 may be distracted and that the secondary vehicle 304or 404 may drive erratically. The on-board computing system 200 may usethe audio emissions 315 to assess the criticality of the situation anddetermine that a collision may occur with a secondary vehicle 304 or404. If the on-board computing system 200 determines that the time tocollision falls below a predetermined threshold then the on-boardcomputing system 200 may stop logging data 12 to first memory 208 andhold the data 12 in second memory 206 until a more opportune to log data12 presents itself.

In accordance with embodiments including such optional sensors, an airand particulate detector may be used to measure air composition through,for example, olfactory analysis, akin to how a human may smell odors inthe air representing impurities. If it is determined that a particularsecondary vehicle 304 or 404 may be emitting excessive exhaust, theon-board computing system 200 may avoid that neighboring vehicle 304 or404. The air and particulate detector may be of any type suitable forperforming chemical or olfactory analysis to detect impurities typicallypresent in air on roadways. The on-board computing system 200 may usethe data 12 collected from the air and particulate detector to avoidneeding to collect more data regarding excessive exhaust from asecondary vehicle 304 or 404. Therefore, the computational load of theon-board computing system 200 could be reduced.

The on-board computing system 200 may use the controller 202 topre-process signals generated by the sensor system 210. For example, thecontroller 202 may apply filters to signals transmitted by the sensorsystem 210 to remove noise and isolate meaningful data using signalprocessing techniques. This could reduce the computational load oflogging data 12 to first memory 208 when the on-board computing system200 deems it an opportune time to log data 12.

FIGS. 9A-9C depict an autonomous vehicle in different driving scenarios.FIG. 9A depicts the autonomous vehicle 10 following a neighboringvehicle 902 at a following distance 904 to the neighboring vehicle 902.The following distance 904 may be used to assess the criticality of thesituation and determine if it is an opportune time to log data 12 tofirst memory 208. The following distance may be used in an evaluation ina potential time to collision assessment. If the on-board computingsystem 200 determines that the time to collision falls below apredetermined threshold amount then the on-board computing system 200may stop logging data 12 to first memory 208. If it is determined that acollision is eminent then the autonomous vehicle 10 may continue to logdata 12 to first memory 208.

FIG. 9B depicts the autonomous vehicle 10 on a roadway 901 in a middlelane 901 b, and suggests that the autonomous vehicle may switch to theleft lane 901 a or the right lane 901 c. There are also neighboringvehicles 908 and 910 in lanes 901 c and 901 a. The assessment of thecriticality of the situation of the autonomous vehicle may determinethat a time to collision falls below a predetermined threshold valuewhen switching to either lanes 901 a, 901 c. In addition, neighboringvehicle 908 or 910 may be driving aggressively and may accelerate anddecelerate quickly. The on-board computing system 200 may determine thata potential time to collision may fall below a threshold value becauseof the aggressive driving nature or a neighboring vehicle 908, 910. As aresult of the time to collision falling below a threshold value, theautonomous vehicle 10 may stop logging data 12 to first memory 208 andhold the data 12 in second memory 206 until a more opportune timepresents itself.

FIG. 9C depicts the autonomous vehicle 10 at an intersection 912, andsuggests that the autonomous vehicle 10 is attempting to execute a leftturn. The on-board computing system 200 may assess the criticality ofthe situation by analyzing all of the objects at the intersection anddetermine that the number of objects in the nearby vicinity exceeds apredetermined threshold value. In addition, pedestrians may be detectedat the intersection 912. The pedestrians may not obey traffic laws andas a result cause the autonomous vehicle 10 to react in a quick mannerto avoid colliding with the pedestrians. Furthermore, the neighboringvehicles 914, 916, 918, and 920 may drive in an aggressive manner. Thismay cause the on-board computing system 200 to assess the criticality ofthe situation and determine the time to collision falls below apredetermined threshold value. A vehicle 918 may be carrying exposedcargo 922, and the autonomous vehicle 10 may predict that some of theexposed cargo 922 may fall off of the vehicle 918 and be a hazard toavoid. As a result of these scenarios, the on-board computing system 200may stop logging data 12 to first memory 208 and hold data 12 in secondmemory 206 until a more opportune time presents itself.

FIG. 10 is a flow diagram 1000 illustrating a methodology for operationof an intelligent logging system in accordance with the presentdisclosures. The illustrative methodology begins with operation 1005, inwhich a controller 202 of the on-board computing system 200 queriessensors 212, 214, 216, 218 and 220 of the sensor system 210. Afterreceiving sensor data 12, controller 202 proceeds with operation 1010and processes sensor data 12. In operation 1015, the controller 202plans actions in response to the sensor data 12. In this illustrativeembodiment, these actions may be related to the functions of theautonomous vehicle 10. In operation 1020, the on-board computing system200 may determine if there is a critical situation in which apredetermined threshold value has been exceeded. The assessment of thecriticality of a situation may be based upon the number of objects inthe vicinity of an autonomous vehicle 10 or a time to collision asdescribed above. If there is a critical situation as a result of apredetermined threshold value being exceeded then operation may continueto operation 1025 and data 12 may be held in second memory 206. If not,operation 1030 may be executed and data 12 may be logged into firstmemory 208.

In operation 1025, there may be an evaluation to see if the data 12 heldin second memory 206 is reaching a critical amount and preventing thecontroller 202 from processing functions for other electrical systems.The sensor data 12 may be held in second memory 206 if it is determinedthat storage in second memory 206 has not reached a critical amount andoperation returns to operation 1005 to restart the process. Thefrequency of the processing may be increased to improve the reactiontime of the autonomous vehicle 10 to potential critical situations.Until operation proceeds to 1030, data may be held in second memory 206.If the controller 202 determines that the data 12 stored on secondmemory 206 has exceeded a determined threshold value then operations mayproceed to operation 1030, and the on-board computing system 200 maystart logging data 12 to first memory 208 to reduce the computationalload on the on-board computing system 200. After data 12 is logged tofirst memory 208, operation 1035 may be executed and the on-boardcomputing system 200 may wait out the rest of the loop and then returnto operation 1005 to restart the process.

The technical problem that arises when logging data 12 into anautonomous vehicle 10 is the gathering of sensor data 12 becomescomputationally intensive. The sensor data 12 is important for systemdebugging and development. As such, the excessive data 12 logging canplace a large burden on the on-board computing system 200 and negativelyimpact performance because file I/O is performance-intensive forcomputers. It may even prevent the autonomous vehicle 10 from operatingin certain complex scenarios, where massive data 12 gathering isrequired to properly develop and debug the system in those situations.

Certain conventional solutions to this problem have been to record lessdata 12 (downsampling or simply leaving out data 12). Other conventionalsolutions to the problem are to invest in additional computing powerdedicated for logging data 12.

To the contrary, the proposed logging innovation approaches the problemin such a way as to provide an improved technical solution bymaintaining the amount of data 12 logged using fewer computationalresources. Sensor data 12 is held in second memory 206 until anassessment of the criticality of the situation determines that it is anopportune time to log data 12 into the first memory 208. The on-boardcomputing system 200 may assess the calculated performance impact oflogging data 12 to the first memory 208 and use that to determine if itis an opportune time to log data 12 to the first memory 208 or hold data12 in the second memory 206. In critical situations (e.g. lots ofsurrounding cars, cyclists, pedestrians) where faster cycle times arerequired, the system may choose to avoid logging data 12 to the firstmemory 208 until an opportune time presents itself. The describedlogging data 12 approach reduces the computational resources neededwhile maintaining the level of data 12 logged for system debugging anddevelopment.

Although certain embodiments have been described and illustrated inexemplary forms with a certain degree of particularity, it is noted thatthe description and illustrations have been made by way of example only.Numerous changes in the details of construction, combination, andarrangement of parts and operations may be made. Accordingly, suchchanges are intended to be included within the scope of the disclosure,the protected scope of which is defined by the claims.

1. An on-board computer system for logging sensor data for an autonomousvehicle, the on-board computer system comprising: a plurality of sensorsfor monitoring sensor data regarding operation of the autonomous vehicleand an environment in which the autonomous vehicle is present; aprocessor for analyzing the sensor data; and a first memory in which themonitored data is stored to log the sensor data received from theplurality of sensors of the autonomous vehicle, wherein the processorassesses criticality of a current situation for the autonomous vehicleby analyzing the sensor data and determines processing capability of theprocessor based on the assessment of criticality of the currentsituation, and wherein the processor includes a means for determiningwhether to log sensor data in the first memory or hold sensor data in asecond memory until an opportune time to log sensor data in the firstmemory based on the determined criticality of the current situation toreduce the load on processing capability of the processor, wherein thefirst memory is a hard drive and the second memory is a RAM.
 2. Theon-board computer system of claim 1, wherein the assessment ofcriticality of the current situation includes analyzing sensor data thatindicates a number of objects in a vicinity of the autonomous vehicle.3. The on-board computer system of claim 2, wherein if the assessment ofcriticality of the current situation indicates the number of objects ina vicinity of the autonomous vehicle exceed a threshold value of numberof objects the processor stops logging data in the first memory andholds sensor data in the second memory until the number of objects fallbelow the threshold value.
 4. The on-board computer system of claim 1,wherein the assessment of criticality of the current situation includesanalyzing sensor data that indicates time to collision of the autonomousvehicle.
 5. The on-board computer system of claim 4, wherein if theassessment of criticality of the current situation indicates the time tocollision of the autonomous vehicle exceeds a threshold value of time tocollision the processor will stop logging data in the first memory andbegin holding sensor data in the second memory until the time tocollision rises above the threshold value.
 6. The on-board computersystem of claim 1, wherein if the processor determines a collision isimminent the processor will continue to log sensor data in the firstmemory.
 7. The on-board computer system of claim 1, wherein a frequencyof the processor logging sensor data varies depending on the assessmentof criticality.
 8. The on-board computer system of claim 1, wherein theprocessor determines whether to log sensor data in the first memory orhold sensor data in a second memory until an opportune time to logsensor data in the first memory based on an evaluation of the determinedcriticality of the current situation and the space of the second memory.9. The on-board computer system of claim 1, wherein the plurality ofsensors comprises Lidar technology.
 10. The on-board computer system ofclaim 1, wherein the processor determines whether to log less sensordata if there is a critical amount of space being reached in the secondmemory.
 11. A method of logging sensor data from a plurality of sensorson an autonomous vehicle, the method comprising: logging sensor datareceived from the plurality of sensors of the autonomous vehicle,wherein the autonomous vehicle includes a computer coupled to theplurality of sensors to receive the sensor data, and the computer has afirst memory, a second memory, and a processor; assessing of criticalityof a current situation for the autonomous vehicle by the processoranalyzing the sensor data; determining processing capability of theprocessor based on the assessment of criticality of the currentsituation; and determining whether to log sensor data in the firstmemory or hold sensor data in the second memory until an opportune timeto log sensor data in the first memory based on the determinedcriticality of the current situation to reduce the load on processingcapability of the processor, wherein the first memory is a hard driveand the second memory is a RAM.
 12. The method of claim 11, wherein theassessment of criticality of the current situation includes analyzingsensor data that indicates a number of objects in a vicinity of theautonomous vehicle.
 13. The method of claim 12, wherein if theassessment of criticality of the current situation indicates the numberof objects in a vicinity of the autonomous vehicle exceed a thresholdvalue of number of objects the processor will stop logging data in thefirst memory and begin holding sensor data in the second memory untilthe number of objects fall below the threshold value.
 14. The method ofclaim 11, wherein the assessment of criticality of the current situationincludes analyzing sensor data that indicates time to collision of theautonomous vehicle.
 15. The method of claim 14, wherein if theassessment of criticality of the current situation indicates the time tocollision of the autonomous vehicle exceeds a threshold value of time tocollision the processor will stop logging data in the first memory andbegin holding sensor data in the second memory until the time tocollision rises above the threshold value.
 16. The method of claim 11,wherein if the processor determines a collision is imminent theprocessor will continue to log sensor data in the first memory.
 17. Themethod of claim 11, wherein a frequency of the processor logging sensordata varies depending on the assessment of criticality.
 18. The methodof claim 11, wherein the processor determines whether to log sensor datain the first memory or hold sensor data in a second memory until anopportune time to log sensor data in the first memory based on anevaluation of the determined criticality of the current situation andthe space of the second memory.
 19. The method of claim 11, wherein theplurality of sensors comprises Lidar technology.
 20. The method of claim11, wherein the processor determines whether to log less sensor data ifthere is a critical amount of space being reached in the second memory.